library(MASS)
library(tidyverse)
library(readr)
library(psych)
library(ggplot2)
library(dplyr)
library(corrplot)
library(RColorBrewer)
library(gridExtra)
library(caret)
library(pROC)
library(car)
# library(MXM)
# library(parallel)
# library(doParallel)TFG Codigo
Librerias
Base de datos
setwd("C:\\Users\\diego\\OneDrive\\Escritorio\\UCM\\Cuarto\\Segundo Cuatri")
datos <- read.csv(file = "application_data.csv")Depuracion de datos
primero vemos cuantas observaciones faltantes hay por columna
data.frame(sort(colSums(is.na(datos)))) sort.colSums.is.na.datos...
SK_ID_CURR 0
TARGET 0
NAME_CONTRACT_TYPE 0
CODE_GENDER 0
FLAG_OWN_CAR 0
FLAG_OWN_REALTY 0
CNT_CHILDREN 0
AMT_INCOME_TOTAL 0
AMT_CREDIT 0
NAME_TYPE_SUITE 0
NAME_INCOME_TYPE 0
NAME_EDUCATION_TYPE 0
NAME_FAMILY_STATUS 0
NAME_HOUSING_TYPE 0
REGION_POPULATION_RELATIVE 0
DAYS_BIRTH 0
DAYS_EMPLOYED 0
DAYS_REGISTRATION 0
DAYS_ID_PUBLISH 0
FLAG_MOBIL 0
FLAG_EMP_PHONE 0
FLAG_WORK_PHONE 0
FLAG_CONT_MOBILE 0
FLAG_PHONE 0
FLAG_EMAIL 0
OCCUPATION_TYPE 0
REGION_RATING_CLIENT 0
REGION_RATING_CLIENT_W_CITY 0
WEEKDAY_APPR_PROCESS_START 0
HOUR_APPR_PROCESS_START 0
REG_REGION_NOT_LIVE_REGION 0
REG_REGION_NOT_WORK_REGION 0
LIVE_REGION_NOT_WORK_REGION 0
REG_CITY_NOT_LIVE_CITY 0
REG_CITY_NOT_WORK_CITY 0
LIVE_CITY_NOT_WORK_CITY 0
ORGANIZATION_TYPE 0
FONDKAPREMONT_MODE 0
HOUSETYPE_MODE 0
WALLSMATERIAL_MODE 0
EMERGENCYSTATE_MODE 0
FLAG_DOCUMENT_2 0
FLAG_DOCUMENT_3 0
FLAG_DOCUMENT_4 0
FLAG_DOCUMENT_5 0
FLAG_DOCUMENT_6 0
FLAG_DOCUMENT_7 0
FLAG_DOCUMENT_8 0
FLAG_DOCUMENT_9 0
FLAG_DOCUMENT_10 0
FLAG_DOCUMENT_11 0
FLAG_DOCUMENT_12 0
FLAG_DOCUMENT_13 0
FLAG_DOCUMENT_14 0
FLAG_DOCUMENT_15 0
FLAG_DOCUMENT_16 0
FLAG_DOCUMENT_17 0
FLAG_DOCUMENT_18 0
FLAG_DOCUMENT_19 0
FLAG_DOCUMENT_20 0
FLAG_DOCUMENT_21 0
DAYS_LAST_PHONE_CHANGE 1
CNT_FAM_MEMBERS 2
AMT_ANNUITY 12
AMT_GOODS_PRICE 278
EXT_SOURCE_2 660
OBS_30_CNT_SOCIAL_CIRCLE 1021
DEF_30_CNT_SOCIAL_CIRCLE 1021
OBS_60_CNT_SOCIAL_CIRCLE 1021
DEF_60_CNT_SOCIAL_CIRCLE 1021
AMT_REQ_CREDIT_BUREAU_HOUR 41519
AMT_REQ_CREDIT_BUREAU_DAY 41519
AMT_REQ_CREDIT_BUREAU_WEEK 41519
AMT_REQ_CREDIT_BUREAU_MON 41519
AMT_REQ_CREDIT_BUREAU_QRT 41519
AMT_REQ_CREDIT_BUREAU_YEAR 41519
EXT_SOURCE_3 60965
TOTALAREA_MODE 148431
YEARS_BEGINEXPLUATATION_AVG 150007
YEARS_BEGINEXPLUATATION_MODE 150007
YEARS_BEGINEXPLUATATION_MEDI 150007
FLOORSMAX_AVG 153020
FLOORSMAX_MODE 153020
FLOORSMAX_MEDI 153020
LIVINGAREA_AVG 154350
LIVINGAREA_MODE 154350
LIVINGAREA_MEDI 154350
ENTRANCES_AVG 154828
ENTRANCES_MODE 154828
ENTRANCES_MEDI 154828
APARTMENTS_AVG 156061
APARTMENTS_MODE 156061
APARTMENTS_MEDI 156061
ELEVATORS_AVG 163891
ELEVATORS_MODE 163891
ELEVATORS_MEDI 163891
NONLIVINGAREA_AVG 169682
NONLIVINGAREA_MODE 169682
NONLIVINGAREA_MEDI 169682
EXT_SOURCE_1 173378
BASEMENTAREA_AVG 179943
BASEMENTAREA_MODE 179943
BASEMENTAREA_MEDI 179943
LANDAREA_AVG 182590
LANDAREA_MODE 182590
LANDAREA_MEDI 182590
OWN_CAR_AGE 202929
YEARS_BUILD_AVG 204488
YEARS_BUILD_MODE 204488
YEARS_BUILD_MEDI 204488
FLOORSMIN_AVG 208642
FLOORSMIN_MODE 208642
FLOORSMIN_MEDI 208642
LIVINGAPARTMENTS_AVG 210199
LIVINGAPARTMENTS_MODE 210199
LIVINGAPARTMENTS_MEDI 210199
NONLIVINGAPARTMENTS_AVG 213514
NONLIVINGAPARTMENTS_MODE 213514
NONLIVINGAPARTMENTS_MEDI 213514
COMMONAREA_AVG 214865
COMMONAREA_MODE 214865
COMMONAREA_MEDI 214865ahora tenemos que ver que hacemos con esas observaciones, hay 2 opciones, eliminar aquellas observaciones o sistituir los valores aplicando reglas sustitutivas
# Calcular el porcentaje de valores nulos por columna
null_datos_df <- datos |>
summarise(across(everything(), ~ sum(is.na(.)) * 100 / n())) |> # control + shift + m
pivot_longer(cols = everything(), names_to = "Column_Name", values_to = "Null_Values_Percentage")
# Crear el gráfico de puntos
ggplot(null_datos_df, aes(x = reorder(Column_Name, -Null_Values_Percentage), y = Null_Values_Percentage)) +
geom_point(color = "blue") +
geom_hline(yintercept = 40, linetype = "dashed", color = "red") + # Línea de referencia al 40%
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, size = 7)) +
labs(title = "Percentage of Missing Values in Application Data",
x = "Columns",
y = "Null Values Percentage")
Variables con mas de un 40 % de datos faltantes
# que columnas tienen mas del 40 % de sus datos missing o NA
# Filtrar columnas con 40% o más de valores nulos
# ponemos como limite un 40 % de datos faltantes, porque sistituir mas de un 40 - 50 % de datos faltantes
# con la mediana o media no es buena idea teniendo tanto % de datos faltantes
nullcol_40_application <- null_datos_df |>
filter(Null_Values_Percentage >= 40)
# Mostrar el resultado
print(nullcol_40_application)# A tibble: 45 × 2
Column_Name Null_Values_Percentage
<chr> <dbl>
1 OWN_CAR_AGE 66.0
2 EXT_SOURCE_1 56.4
3 APARTMENTS_AVG 50.7
4 BASEMENTAREA_AVG 58.5
5 YEARS_BEGINEXPLUATATION_AVG 48.8
6 YEARS_BUILD_AVG 66.5
7 COMMONAREA_AVG 69.9
8 ELEVATORS_AVG 53.3
9 ENTRANCES_AVG 50.3
10 FLOORSMAX_AVG 49.8
# ℹ 35 more rowsDatos faltantes
cuantos datos faltantes tenemos por columna
categorical_columns <- c('NAME_CONTRACT_TYPE', 'CODE_GENDER', 'NAME_TYPE_SUITE', 'NAME_INCOME_TYPE', 'NAME_EDUCATION_TYPE', 'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE', 'OCCUPATION_TYPE', 'WEEKDAY_APPR_PROCESS_START','ORGANIZATION_TYPE', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY', 'LIVE_CITY_NOT_WORK_CITY', 'REG_CITY_NOT_LIVE_CITY', 'REG_CITY_NOT_WORK_CITY', 'REG_REGION_NOT_WORK_REGION','LIVE_REGION_NOT_WORK_REGION', 'REGION_RATING_CLIENT','REGION_RATING_CLIENT_W_CITY')
contact_col <- c("FLAG_MOBIL", "FLAG_EMP_PHONE", "FLAG_WORK_PHONE",
"FLAG_CONT_MOBILE", "FLAG_PHONE", "FLAG_EMAIL")
col_Doc <- c("FLAG_DOCUMENT_2", "FLAG_DOCUMENT_3", "FLAG_DOCUMENT_4", "FLAG_DOCUMENT_5", "FLAG_DOCUMENT_6",
"FLAG_DOCUMENT_7", "FLAG_DOCUMENT_8", "FLAG_DOCUMENT_9", "FLAG_DOCUMENT_10", "FLAG_DOCUMENT_11",
"FLAG_DOCUMENT_12", "FLAG_DOCUMENT_13", "FLAG_DOCUMENT_14", "FLAG_DOCUMENT_15", "FLAG_DOCUMENT_16",
"FLAG_DOCUMENT_17", "FLAG_DOCUMENT_18", "FLAG_DOCUMENT_19", "FLAG_DOCUMENT_20", "FLAG_DOCUMENT_21")
ext <- c("EXT_SOURCE_1", "EXT_SOURCE_2", "EXT_SOURCE_3")
data.frame(sort(colSums(is.na(datos)))) sort.colSums.is.na.datos...
SK_ID_CURR 0
TARGET 0
NAME_CONTRACT_TYPE 0
CODE_GENDER 0
FLAG_OWN_CAR 0
FLAG_OWN_REALTY 0
CNT_CHILDREN 0
AMT_INCOME_TOTAL 0
AMT_CREDIT 0
NAME_TYPE_SUITE 0
NAME_INCOME_TYPE 0
NAME_EDUCATION_TYPE 0
NAME_FAMILY_STATUS 0
NAME_HOUSING_TYPE 0
REGION_POPULATION_RELATIVE 0
DAYS_BIRTH 0
DAYS_EMPLOYED 0
DAYS_REGISTRATION 0
DAYS_ID_PUBLISH 0
FLAG_MOBIL 0
FLAG_EMP_PHONE 0
FLAG_WORK_PHONE 0
FLAG_CONT_MOBILE 0
FLAG_PHONE 0
FLAG_EMAIL 0
OCCUPATION_TYPE 0
REGION_RATING_CLIENT 0
REGION_RATING_CLIENT_W_CITY 0
WEEKDAY_APPR_PROCESS_START 0
HOUR_APPR_PROCESS_START 0
REG_REGION_NOT_LIVE_REGION 0
REG_REGION_NOT_WORK_REGION 0
LIVE_REGION_NOT_WORK_REGION 0
REG_CITY_NOT_LIVE_CITY 0
REG_CITY_NOT_WORK_CITY 0
LIVE_CITY_NOT_WORK_CITY 0
ORGANIZATION_TYPE 0
FONDKAPREMONT_MODE 0
HOUSETYPE_MODE 0
WALLSMATERIAL_MODE 0
EMERGENCYSTATE_MODE 0
FLAG_DOCUMENT_2 0
FLAG_DOCUMENT_3 0
FLAG_DOCUMENT_4 0
FLAG_DOCUMENT_5 0
FLAG_DOCUMENT_6 0
FLAG_DOCUMENT_7 0
FLAG_DOCUMENT_8 0
FLAG_DOCUMENT_9 0
FLAG_DOCUMENT_10 0
FLAG_DOCUMENT_11 0
FLAG_DOCUMENT_12 0
FLAG_DOCUMENT_13 0
FLAG_DOCUMENT_14 0
FLAG_DOCUMENT_15 0
FLAG_DOCUMENT_16 0
FLAG_DOCUMENT_17 0
FLAG_DOCUMENT_18 0
FLAG_DOCUMENT_19 0
FLAG_DOCUMENT_20 0
FLAG_DOCUMENT_21 0
DAYS_LAST_PHONE_CHANGE 1
CNT_FAM_MEMBERS 2
AMT_ANNUITY 12
AMT_GOODS_PRICE 278
EXT_SOURCE_2 660
OBS_30_CNT_SOCIAL_CIRCLE 1021
DEF_30_CNT_SOCIAL_CIRCLE 1021
OBS_60_CNT_SOCIAL_CIRCLE 1021
DEF_60_CNT_SOCIAL_CIRCLE 1021
AMT_REQ_CREDIT_BUREAU_HOUR 41519
AMT_REQ_CREDIT_BUREAU_DAY 41519
AMT_REQ_CREDIT_BUREAU_WEEK 41519
AMT_REQ_CREDIT_BUREAU_MON 41519
AMT_REQ_CREDIT_BUREAU_QRT 41519
AMT_REQ_CREDIT_BUREAU_YEAR 41519
EXT_SOURCE_3 60965
TOTALAREA_MODE 148431
YEARS_BEGINEXPLUATATION_AVG 150007
YEARS_BEGINEXPLUATATION_MODE 150007
YEARS_BEGINEXPLUATATION_MEDI 150007
FLOORSMAX_AVG 153020
FLOORSMAX_MODE 153020
FLOORSMAX_MEDI 153020
LIVINGAREA_AVG 154350
LIVINGAREA_MODE 154350
LIVINGAREA_MEDI 154350
ENTRANCES_AVG 154828
ENTRANCES_MODE 154828
ENTRANCES_MEDI 154828
APARTMENTS_AVG 156061
APARTMENTS_MODE 156061
APARTMENTS_MEDI 156061
ELEVATORS_AVG 163891
ELEVATORS_MODE 163891
ELEVATORS_MEDI 163891
NONLIVINGAREA_AVG 169682
NONLIVINGAREA_MODE 169682
NONLIVINGAREA_MEDI 169682
EXT_SOURCE_1 173378
BASEMENTAREA_AVG 179943
BASEMENTAREA_MODE 179943
BASEMENTAREA_MEDI 179943
LANDAREA_AVG 182590
LANDAREA_MODE 182590
LANDAREA_MEDI 182590
OWN_CAR_AGE 202929
YEARS_BUILD_AVG 204488
YEARS_BUILD_MODE 204488
YEARS_BUILD_MEDI 204488
FLOORSMIN_AVG 208642
FLOORSMIN_MODE 208642
FLOORSMIN_MEDI 208642
LIVINGAPARTMENTS_AVG 210199
LIVINGAPARTMENTS_MODE 210199
LIVINGAPARTMENTS_MEDI 210199
NONLIVINGAPARTMENTS_AVG 213514
NONLIVINGAPARTMENTS_MODE 213514
NONLIVINGAPARTMENTS_MEDI 213514
COMMONAREA_AVG 214865
COMMONAREA_MODE 214865
COMMONAREA_MEDI 214865# Convertir las columnas a factor (categóricas)
datos[categorical_columns] <- lapply(datos[categorical_columns], as.factor)Factorizamos las variables contacto y otras que sean necesarias
datos <- datos %>%
mutate(across(all_of(contact_col), as.factor)) %>%
mutate(across(all_of(col_Doc), as.factor))variables categoricas
con pocos datos faltantes (moda)
# Función para imputar valores faltantes con la moda
imputar_moda <- function(x) {
if (is.factor(x) | is.character(x)) { # Verifica si es categórica
moda <- names(sort(table(x), decreasing = TRUE))[1] # Encuentra la moda
x[is.na(x)] <- moda # Reemplaza los NA con la moda
}
return(x)
}#categorical_columns <- c(categorical_columns,"AMT_INCOME_RANGE")
# Aplicar la función a todas las columnas categóricas
datos[categorical_columns] <- lapply(datos[categorical_columns], imputar_moda)variables numericas
para sustituir aquellas variables que son numericas y tienen una observacion faltante, haremos uso de la media.
distribucion_variables_numericas <- function(datos) {
numeric_columns <- datos |> select_if(is.numeric) |> names() # Selecciona las variables numéricas
for (col in numeric_columns) {
cat("\n-------------------------------------------------\n")
cat("Distribución de la variable:", col, "\n")
cat("-------------------------------------------------\n")
print(summary(datos[[col]])) # Resumen estadístico
hist(datos[[col]], main = paste("Histograma de", col), col = "skyblue", border = "white", xlab = col)
# Test de Kolmogorov-Smirnov para normalidad
ks_test <- ks.test(datos[[col]], "pnorm", mean(datos[[col]], na.rm = TRUE), sd(datos[[col]], na.rm = TRUE))
cat("\nTest de Kolmogorov-Smirnov para la normalidad:\n")
print(ks_test)
if (ks_test$p.value < 0.05) {
cat("❌ La variable", col, "NO sigue una distribución normal (p <", ks_test$p.value, ")\n")
} else {
cat("✅ La variable", col, "SIGUE una distribución normal (p =", ks_test$p.value, ")\n")
}
}
}
# Llamada a la función
distribucion_variables_numericas(datos)
-------------------------------------------------
Distribución de la variable: SK_ID_CURR
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max.
100002 189146 278202 278181 367143 456255 
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.057265, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable SK_ID_CURR NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: TARGET
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00000 0.00000 0.00000 0.08073 0.00000 1.00000 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.53579, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable TARGET NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: CNT_CHILDREN
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.0000 0.0000 0.4171 1.0000 19.0000 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.41858, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable CNT_CHILDREN NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: AMT_INCOME_TOTAL
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max.
25650 112500 147150 168798 202500 117000000 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.30171, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable AMT_INCOME_TOTAL NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: AMT_CREDIT
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max.
45000 270000 513531 599026 808650 4050000 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.11015, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable AMT_CREDIT NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: AMT_ANNUITY
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
1616 16524 24903 27109 34596 258026 12 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.0789, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable AMT_ANNUITY NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: AMT_GOODS_PRICE
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
40500 238500 450000 538396 679500 4050000 278 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.14269, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable AMT_GOODS_PRICE NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: REGION_POPULATION_RELATIVE
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00029 0.01001 0.01885 0.02087 0.02866 0.07251 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.11345, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable REGION_POPULATION_RELATIVE NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: DAYS_BIRTH
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max.
-25229 -19682 -15750 -16037 -12413 -7489 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.048582, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable DAYS_BIRTH NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: DAYS_EMPLOYED
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max.
-17912 -2760 -1213 63815 -289 365243 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.49419, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable DAYS_EMPLOYED NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: DAYS_REGISTRATION
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max.
-24672 -7480 -4504 -4986 -2010 0 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.078483, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable DAYS_REGISTRATION NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: DAYS_ID_PUBLISH
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max.
-7197 -4299 -3254 -2994 -1720 0 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.12221, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable DAYS_ID_PUBLISH NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: OWN_CAR_AGE
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 5.00 9.00 12.06 15.00 91.00 202929 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.16271, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable OWN_CAR_AGE NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: CNT_FAM_MEMBERS
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
1.000 2.000 2.000 2.153 3.000 20.000 2 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.30217, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable CNT_FAM_MEMBERS NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: HOUR_APPR_PROCESS_START
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00 10.00 12.00 12.06 14.00 23.00 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.08234, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable HOUR_APPR_PROCESS_START NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: REG_REGION_NOT_LIVE_REGION
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00000 0.00000 0.00000 0.01514 0.00000 1.00000 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.5342, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable REG_REGION_NOT_LIVE_REGION NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: EXT_SOURCE_1
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.01 0.33 0.51 0.50 0.68 0.96 173378 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.044677, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable EXT_SOURCE_1 NO sigue una distribución normal (p < 5.58411e-233 )
-------------------------------------------------
Distribución de la variable: EXT_SOURCE_2
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0000 0.3925 0.5660 0.5144 0.6636 0.8550 660 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.10691, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable EXT_SOURCE_2 NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: EXT_SOURCE_3
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.37 0.54 0.51 0.67 0.90 60965 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.061755, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable EXT_SOURCE_3 NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: APARTMENTS_AVG
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.06 0.09 0.12 0.15 1.00 156061 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.1668, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable APARTMENTS_AVG NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: BASEMENTAREA_AVG
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.04 0.08 0.09 0.11 1.00 179943 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.14167, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable BASEMENTAREA_AVG NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: YEARS_BEGINEXPLUATATION_AVG
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.98 0.98 0.98 0.99 1.00 150007 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.39064, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable YEARS_BEGINEXPLUATATION_AVG NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: YEARS_BUILD_AVG
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.69 0.76 0.75 0.82 1.00 204488 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.051642, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable YEARS_BUILD_AVG NO sigue una distribución normal (p < 4.560853e-239 )
-------------------------------------------------
Distribución de la variable: COMMONAREA_AVG
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.01 0.02 0.04 0.05 1.00 214865 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.27866, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable COMMONAREA_AVG NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: ELEVATORS_AVG
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.00 0.00 0.08 0.12 1.00 163891 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.3181, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable ELEVATORS_AVG NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: ENTRANCES_AVG
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.07 0.14 0.15 0.21 1.00 154828 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.19338, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable ENTRANCES_AVG NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: FLOORSMAX_AVG
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.17 0.17 0.23 0.33 1.00 153020 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.27317, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable FLOORSMAX_AVG NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: FLOORSMIN_AVG
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.08 0.21 0.23 0.38 1.00 208642 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.22705, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable FLOORSMIN_AVG NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: LANDAREA_AVG
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.02 0.05 0.07 0.09 1.00 182590 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.20694, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable LANDAREA_AVG NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: LIVINGAPARTMENTS_AVG
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.05 0.08 0.10 0.12 1.00 210199 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.17467, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable LIVINGAPARTMENTS_AVG NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: LIVINGAREA_AVG
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.05 0.07 0.11 0.13 1.00 154350 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.18232, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable LIVINGAREA_AVG NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: NONLIVINGAPARTMENTS_AVG
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.00 0.00 0.01 0.00 1.00 213514 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.42679, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable NONLIVINGAPARTMENTS_AVG NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: NONLIVINGAREA_AVG
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.00 0.00 0.03 0.03 1.00 169682 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.34168, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable NONLIVINGAREA_AVG NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: APARTMENTS_MODE
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.05 0.08 0.11 0.14 1.00 156061 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.17123, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable APARTMENTS_MODE NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: BASEMENTAREA_MODE
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.04 0.07 0.09 0.11 1.00 179943 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.14955, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable BASEMENTAREA_MODE NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: YEARS_BEGINEXPLUATATION_MODE
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.98 0.98 0.98 0.99 1.00 150007 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.39761, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable YEARS_BEGINEXPLUATATION_MODE NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: YEARS_BUILD_MODE
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.70 0.76 0.76 0.82 1.00 204488 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.054756, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable YEARS_BUILD_MODE NO sigue una distribución normal (p < 1.021391e-268 )
-------------------------------------------------
Distribución de la variable: COMMONAREA_MODE
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.01 0.02 0.04 0.05 1.00 214865 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.28379, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable COMMONAREA_MODE NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: ELEVATORS_MODE
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.00 0.00 0.07 0.12 1.00 163891 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.33652, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable ELEVATORS_MODE NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: ENTRANCES_MODE
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.07 0.14 0.15 0.21 1.00 154828 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.204, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable ENTRANCES_MODE NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: FLOORSMAX_MODE
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.17 0.17 0.22 0.33 1.00 153020 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.28906, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable FLOORSMAX_MODE NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: FLOORSMIN_MODE
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.08 0.21 0.23 0.38 1.00 208642 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.23649, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable FLOORSMIN_MODE NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: LANDAREA_MODE
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.02 0.05 0.06 0.08 1.00 182590 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.21343, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable LANDAREA_MODE NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: LIVINGAPARTMENTS_MODE
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.05 0.08 0.11 0.13 1.00 210199 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.17894, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable LIVINGAPARTMENTS_MODE NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: LIVINGAREA_MODE
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.04 0.07 0.11 0.13 1.00 154350 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.19075, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable LIVINGAREA_MODE NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: NONLIVINGAPARTMENTS_MODE
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.00 0.00 0.01 0.00 1.00 213514 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.43073, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable NONLIVINGAPARTMENTS_MODE NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: NONLIVINGAREA_MODE
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.00 0.00 0.03 0.02 1.00 169682 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.35025, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable NONLIVINGAREA_MODE NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: APARTMENTS_MEDI
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.06 0.09 0.12 0.15 1.00 156061 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.16968, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable APARTMENTS_MEDI NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: BASEMENTAREA_MEDI
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.04 0.08 0.09 0.11 1.00 179943 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.14225, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable BASEMENTAREA_MEDI NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: YEARS_BEGINEXPLUATATION_MEDI
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.98 0.98 0.98 0.99 1.00 150007 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.39156, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable YEARS_BEGINEXPLUATATION_MEDI NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: YEARS_BUILD_MEDI
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.69 0.76 0.76 0.83 1.00 204488 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.051814, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable YEARS_BUILD_MEDI NO sigue una distribución normal (p < 1.165368e-240 )
-------------------------------------------------
Distribución de la variable: COMMONAREA_MEDI
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.01 0.02 0.04 0.05 1.00 214865 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.27905, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable COMMONAREA_MEDI NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: ELEVATORS_MEDI
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.00 0.00 0.08 0.12 1.00 163891 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.32521, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable ELEVATORS_MEDI NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: ENTRANCES_MEDI
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.07 0.14 0.15 0.21 1.00 154828 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.19915, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable ENTRANCES_MEDI NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: FLOORSMAX_MEDI
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.17 0.17 0.23 0.33 1.00 153020 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.28113, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable FLOORSMAX_MEDI NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: FLOORSMIN_MEDI
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.08 0.21 0.23 0.38 1.00 208642 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.23289, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable FLOORSMIN_MEDI NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: LANDAREA_MEDI
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.02 0.05 0.07 0.09 1.00 182590 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.20683, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable LANDAREA_MEDI NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: LIVINGAPARTMENTS_MEDI
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.05 0.08 0.10 0.12 1.00 210199 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.17714, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable LIVINGAPARTMENTS_MEDI NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: LIVINGAREA_MEDI
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.05 0.07 0.11 0.13 1.00 154350 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.18396, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable LIVINGAREA_MEDI NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: NONLIVINGAPARTMENTS_MEDI
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.00 0.00 0.01 0.00 1.00 213514 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.42761, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable NONLIVINGAPARTMENTS_MEDI NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: NONLIVINGAREA_MEDI
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.00 0.00 0.03 0.03 1.00 169682 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.34369, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable NONLIVINGAREA_MEDI NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: TOTALAREA_MODE
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.04 0.07 0.10 0.13 1.00 148431 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.18429, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable TOTALAREA_MODE NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: OBS_30_CNT_SOCIAL_CIRCLE
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 0.000 0.000 1.422 2.000 348.000 1021 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.27681, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable OBS_30_CNT_SOCIAL_CIRCLE NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: DEF_30_CNT_SOCIAL_CIRCLE
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0000 0.0000 0.0000 0.1434 0.0000 34.0000 1021 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.51118, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable DEF_30_CNT_SOCIAL_CIRCLE NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: OBS_60_CNT_SOCIAL_CIRCLE
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 0.000 0.000 1.405 2.000 344.000 1021 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.27743, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable OBS_60_CNT_SOCIAL_CIRCLE NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: DEF_60_CNT_SOCIAL_CIRCLE
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0 0.0 0.0 0.1 0.0 24.0 1021 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.52471, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable DEF_60_CNT_SOCIAL_CIRCLE NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: DAYS_LAST_PHONE_CHANGE
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-4292.0 -1570.0 -757.0 -962.9 -274.0 0.0 1 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.1221, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable DAYS_LAST_PHONE_CHANGE NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: AMT_REQ_CREDIT_BUREAU_HOUR
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.00 0.00 0.01 0.00 4.00 41519 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.52432, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable AMT_REQ_CREDIT_BUREAU_HOUR NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: AMT_REQ_CREDIT_BUREAU_DAY
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.00 0.00 0.01 0.00 9.00 41519 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.5196, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable AMT_REQ_CREDIT_BUREAU_DAY NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: AMT_REQ_CREDIT_BUREAU_WEEK
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.00 0.00 0.03 0.00 8.00 41519 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.53457, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable AMT_REQ_CREDIT_BUREAU_WEEK NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: AMT_REQ_CREDIT_BUREAU_MON
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.00 0.00 0.27 0.00 27.00 41519 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.45031, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable AMT_REQ_CREDIT_BUREAU_MON NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: AMT_REQ_CREDIT_BUREAU_QRT
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.00 0.00 0.27 0.00 261.00 41519 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.4408, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable AMT_REQ_CREDIT_BUREAU_QRT NO sigue una distribución normal (p < 0 )
-------------------------------------------------
Distribución de la variable: AMT_REQ_CREDIT_BUREAU_YEAR
-------------------------------------------------
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0 0.0 1.0 1.9 3.0 25.0 41519 Warning in ks.test.default(datos[[col]], "pnorm", mean(datos[[col]], na.rm =
TRUE), : ties should not be present for the one-sample Kolmogorov-Smirnov test
Test de Kolmogorov-Smirnov para la normalidad:
Asymptotic one-sample Kolmogorov-Smirnov test
data: datos[[col]]
D = 0.19321, p-value < 2.2e-16
alternative hypothesis: two-sided
❌ La variable AMT_REQ_CREDIT_BUREAU_YEAR NO sigue una distribución normal (p < 0 ) # Función para imputar valores faltantes con la media
imputar_mediana <- function(x) {
if (is.numeric(x)) { # Verifica si es numérica
x[is.na(x)] <- median(x, na.rm = TRUE) # Calcula y reemplaza con la media
}
return(x)
}numeric_columns <- datos |> select_if(is.numeric) |> names()
# Aplicar la función a todas las columnas numéricas
datos[numeric_columns] <- lapply(datos[numeric_columns], imputar_mediana)data.frame(sort(colSums(is.na(datos)))) sort.colSums.is.na.datos...
SK_ID_CURR 0
TARGET 0
NAME_CONTRACT_TYPE 0
CODE_GENDER 0
FLAG_OWN_CAR 0
FLAG_OWN_REALTY 0
CNT_CHILDREN 0
AMT_INCOME_TOTAL 0
AMT_CREDIT 0
AMT_ANNUITY 0
AMT_GOODS_PRICE 0
NAME_TYPE_SUITE 0
NAME_INCOME_TYPE 0
NAME_EDUCATION_TYPE 0
NAME_FAMILY_STATUS 0
NAME_HOUSING_TYPE 0
REGION_POPULATION_RELATIVE 0
DAYS_BIRTH 0
DAYS_EMPLOYED 0
DAYS_REGISTRATION 0
DAYS_ID_PUBLISH 0
OWN_CAR_AGE 0
FLAG_MOBIL 0
FLAG_EMP_PHONE 0
FLAG_WORK_PHONE 0
FLAG_CONT_MOBILE 0
FLAG_PHONE 0
FLAG_EMAIL 0
OCCUPATION_TYPE 0
CNT_FAM_MEMBERS 0
REGION_RATING_CLIENT 0
REGION_RATING_CLIENT_W_CITY 0
WEEKDAY_APPR_PROCESS_START 0
HOUR_APPR_PROCESS_START 0
REG_REGION_NOT_LIVE_REGION 0
REG_REGION_NOT_WORK_REGION 0
LIVE_REGION_NOT_WORK_REGION 0
REG_CITY_NOT_LIVE_CITY 0
REG_CITY_NOT_WORK_CITY 0
LIVE_CITY_NOT_WORK_CITY 0
ORGANIZATION_TYPE 0
EXT_SOURCE_1 0
EXT_SOURCE_2 0
EXT_SOURCE_3 0
APARTMENTS_AVG 0
BASEMENTAREA_AVG 0
YEARS_BEGINEXPLUATATION_AVG 0
YEARS_BUILD_AVG 0
COMMONAREA_AVG 0
ELEVATORS_AVG 0
ENTRANCES_AVG 0
FLOORSMAX_AVG 0
FLOORSMIN_AVG 0
LANDAREA_AVG 0
LIVINGAPARTMENTS_AVG 0
LIVINGAREA_AVG 0
NONLIVINGAPARTMENTS_AVG 0
NONLIVINGAREA_AVG 0
APARTMENTS_MODE 0
BASEMENTAREA_MODE 0
YEARS_BEGINEXPLUATATION_MODE 0
YEARS_BUILD_MODE 0
COMMONAREA_MODE 0
ELEVATORS_MODE 0
ENTRANCES_MODE 0
FLOORSMAX_MODE 0
FLOORSMIN_MODE 0
LANDAREA_MODE 0
LIVINGAPARTMENTS_MODE 0
LIVINGAREA_MODE 0
NONLIVINGAPARTMENTS_MODE 0
NONLIVINGAREA_MODE 0
APARTMENTS_MEDI 0
BASEMENTAREA_MEDI 0
YEARS_BEGINEXPLUATATION_MEDI 0
YEARS_BUILD_MEDI 0
COMMONAREA_MEDI 0
ELEVATORS_MEDI 0
ENTRANCES_MEDI 0
FLOORSMAX_MEDI 0
FLOORSMIN_MEDI 0
LANDAREA_MEDI 0
LIVINGAPARTMENTS_MEDI 0
LIVINGAREA_MEDI 0
NONLIVINGAPARTMENTS_MEDI 0
NONLIVINGAREA_MEDI 0
FONDKAPREMONT_MODE 0
HOUSETYPE_MODE 0
TOTALAREA_MODE 0
WALLSMATERIAL_MODE 0
EMERGENCYSTATE_MODE 0
OBS_30_CNT_SOCIAL_CIRCLE 0
DEF_30_CNT_SOCIAL_CIRCLE 0
OBS_60_CNT_SOCIAL_CIRCLE 0
DEF_60_CNT_SOCIAL_CIRCLE 0
DAYS_LAST_PHONE_CHANGE 0
FLAG_DOCUMENT_2 0
FLAG_DOCUMENT_3 0
FLAG_DOCUMENT_4 0
FLAG_DOCUMENT_5 0
FLAG_DOCUMENT_6 0
FLAG_DOCUMENT_7 0
FLAG_DOCUMENT_8 0
FLAG_DOCUMENT_9 0
FLAG_DOCUMENT_10 0
FLAG_DOCUMENT_11 0
FLAG_DOCUMENT_12 0
FLAG_DOCUMENT_13 0
FLAG_DOCUMENT_14 0
FLAG_DOCUMENT_15 0
FLAG_DOCUMENT_16 0
FLAG_DOCUMENT_17 0
FLAG_DOCUMENT_18 0
FLAG_DOCUMENT_19 0
FLAG_DOCUMENT_20 0
FLAG_DOCUMENT_21 0
AMT_REQ_CREDIT_BUREAU_HOUR 0
AMT_REQ_CREDIT_BUREAU_DAY 0
AMT_REQ_CREDIT_BUREAU_WEEK 0
AMT_REQ_CREDIT_BUREAU_MON 0
AMT_REQ_CREDIT_BUREAU_QRT 0
AMT_REQ_CREDIT_BUREAU_YEAR 0Estandarizar valores
Primero pasamos las columnas con dias negativos a positivos
# Lista de columnas con días negativos
date_col <- c("DAYS_BIRTH", "DAYS_EMPLOYED", "DAYS_REGISTRATION", "DAYS_ID_PUBLISH")
# Convertir valores negativos a positivos en todas las columnas de la lista
datos[date_col] <- abs(datos[date_col])Ahora vamos a organizar a las personas segun su nivel de ingresos (Dicotomizamos)
# Dividir AMT_INCOME_TOTAL por 100,000
datos$AMT_INCOME_TOTAL <- datos$AMT_INCOME_TOTAL / 100000
# Definir los límites de los bins
bins <- c(0,1,2,3,4,5,6,7,8,9,10,11)
# Definir las etiquetas para los rangos de ingresos
slot <- c('0-100K','100K-200K', '200K-300K','300K-400K','400K-500K',
'500K-600K','600K-700K','700K-800K','800K-900K','900K-1M', '1M Above')
# Crear la nueva variable categórica usando cut()
datos$AMT_INCOME_RANGE <- cut(datos$AMT_INCOME_TOTAL, breaks = bins, labels = slot, include.lowest = TRUE)
# Calcular la frecuencia relativa (%) de cada categoría en AMT_INCOME_RANGE
prop.table(table(datos$AMT_INCOME_RANGE)) * 100
0-100K 100K-200K 200K-300K 300K-400K 400K-500K 500K-600K
20.729695163 50.734999788 21.210691261 4.776115517 1.744668526 0.356353672
600K-700K 700K-800K 800K-900K 900K-1M 1M Above
0.282804878 0.052720817 0.096980269 0.009112240 0.005857869 Relaizamos lo mismo para la cantida de credito, la edad y las horas trabajadas para facilitar las comparaciones en el futuro
# Dividir AMT_CREDIT por 100,000
datos$AMT_CREDIT <- datos$AMT_CREDIT / 100000
# Definir los límites de los bins
bins <- c(0,1,2,3,4,5,6,7,8,9,10,100)
# Definir las etiquetas para los rangos de crédito
slots <- c('0-100K','100K-200K', '200K-300K','300K-400K','400K-500K',
'500K-600K','600K-700K','700K-800K','800K-900K','900K-1M', '1M Above')
# Crear la nueva variable categórica
datos$AMT_CREDIT_RANGE <- cut(datos$AMT_CREDIT, breaks = bins, labels = slots, include.lowest = TRUE)
# Calcular la frecuencia relativa (%) de cada categoría en AMT_CREDIT_RANGE
prop.table(table(datos$AMT_CREDIT_RANGE)) * 100
0-100K 100K-200K 200K-300K 300K-400K 400K-500K 500K-600K 600K-700K 700K-800K
1.952450 9.801275 17.824728 8.564897 10.418489 11.131960 7.820533 6.241403
800K-900K 900K-1M 1M Above
7.086576 2.902986 16.254703 # Crear la variable AGE a partir de DAYS_BIRTH
datos$AGE <- floor(abs(datos$DAYS_BIRTH) / 365)
# Definir los límites de los bins
bins <- c(0, 20, 30, 40, 50, 100)
# Definir las etiquetas para los grupos de edad
slots <- c('0-20', '20-30', '30-40', '40-50', '50 above')
# Crear la nueva variable categórica
datos$AGE_GROUP <- cut(datos$AGE, breaks = bins, labels = slots, include.lowest = TRUE)
# Calcular la frecuencia relativa (%) de cada categoría en AGE_GROUP
prop.table(table(datos$AGE_GROUP)) * 100
0-20 20-30 30-40 40-50 50 above
3.251916e-04 1.717174e+01 2.702895e+01 2.419458e+01 3.160440e+01 datos$AGE <- floor(abs(datos$DAYS_BIRTH) / 365)# Crear la variable YEARS_EMPLOYED a partir de DAYS_EMPLOYED
datos$YEARS_EMPLOYED <- floor(abs(datos$DAYS_EMPLOYED) / 365)
# Definir los límites de los bins
bins <- c(0, 5, 10, 20, 30, 40, 50, 60, 150)
# Definir las etiquetas para los grupos de años de empleo
slots <- c('0-5', '5-10', '10-20', '20-30', '30-40', '40-50', '50-60', '60 above')
# Crear la nueva variable categórica
datos$EMPLOYMENT_YEAR <- cut(datos$YEARS_EMPLOYED, breaks = bins, labels = slots, include.lowest = TRUE)
# Calcular la frecuencia relativa (%) de cada categoría en EMPLOYMENT_YEAR
prop.table(table(datos$EMPLOYMENT_YEAR)) * 100
0-5 5-10 10-20 20-30 30-40 40-50
60.49806256 22.20340529 12.95248218 3.33509164 0.94155162 0.06940671
50-60 60 above
0.00000000 0.00000000 Se lleva a cabo esto para poder facilitar la comparacion entre observaciones y la clasificacion de modelos. Viendo la diferencia entre los distintos grupos
L1 PENALTY PARA LA REGRESION USAR apuntaría brevemente en cada caso, que puedes hacer para seguir
#TIPIFICAR TODAS LAS VARIABLES NUMERICAS Factorial de variables
Variables economicas
economic_vars <- datos[, c("AMT_INCOME_TOTAL", "AMT_CREDIT", "AMT_ANNUITY", "AMT_GOODS_PRICE","OWN_CAR_AGE","DAYS_EMPLOYED")]
#"CNT_FAM_MEMBERS" "CNT_CHILDREN"
economic_vars_scaled <- scale(economic_vars)
factor_analysis <- factanal(economic_vars_scaled, factors = 2, rotation = "varimax")
print(factor_analysis, digits = 3, cutoff = 0.3, sort = TRUE)
Call:
factanal(x = economic_vars_scaled, factors = 2, rotation = "varimax")
Uniquenesses:
AMT_INCOME_TOTAL AMT_CREDIT AMT_ANNUITY AMT_GOODS_PRICE
0.908 0.020 0.328 0.006
OWN_CAR_AGE DAYS_EMPLOYED
0.999 0.953
Loadings:
Factor1 Factor2
AMT_CREDIT 0.973
AMT_ANNUITY 0.717 0.398
AMT_GOODS_PRICE 0.980
AMT_INCOME_TOTAL
OWN_CAR_AGE
DAYS_EMPLOYED
Factor1 Factor2
SS loadings 2.436 0.351
Proportion Var 0.406 0.059
Cumulative Var 0.406 0.464
Test of the hypothesis that 2 factors are sufficient.
The chi square statistic is 671.06 on 4 degrees of freedom.
The p-value is 6.43e-144 print(factor_analysis$loadings)
Loadings:
Factor1 Factor2
AMT_INCOME_TOTAL 0.110 0.283
AMT_CREDIT 0.973 0.182
AMT_ANNUITY 0.717 0.398
AMT_GOODS_PRICE 0.980 0.181
OWN_CAR_AGE
DAYS_EMPLOYED -0.216
Factor1 Factor2
SS loadings 2.436 0.351
Proportion Var 0.406 0.059
Cumulative Var 0.406 0.464print("------------------------- KMO -----------------------------------")[1] "------------------------- KMO -----------------------------------"KMO(economic_vars_scaled) # Índice de adecuación muestralKaiser-Meyer-Olkin factor adequacy
Call: KMO(r = economic_vars_scaled)
Overall MSA = 0.7
MSA for each item =
AMT_INCOME_TOTAL AMT_CREDIT AMT_ANNUITY AMT_GOODS_PRICE
0.87 0.63 0.97 0.63
OWN_CAR_AGE DAYS_EMPLOYED
0.61 0.70 cortest.bartlett(economic_vars_scaled) # Prueba de esfericidad de BartlettR was not square, finding R from data$chisq
[1] 1417942
$p.value
[1] 0
$df
[1] 15print("------------------------ loadings ------------------------------------")[1] "------------------------ loadings ------------------------------------"loadings <- as.data.frame(factor_analysis$loadings[,1:2])
loadings$Variable <- rownames(loadings)
print("-------------------------- ggplot ----------------------------------")[1] "-------------------------- ggplot ----------------------------------"pca_result <- prcomp(economic_vars_scaled, scale = TRUE)
screeplot(pca_result, type = "lines", main = "Scree Plot")
ggplot(loadings, aes(x = Factor1, y = Factor2, label = Variable)) +
geom_text(size = 5) +
theme_minimal() +
ggtitle("Carga Factorial de Variables Económicas")
Valores atipicos
# Definir las variables para analizar outliers
app_outlier_col_1 <- c('AMT_ANNUITY', 'AMT_INCOME_TOTAL', 'AMT_CREDIT', 'AMT_GOODS_PRICE', 'DAYS_EMPLOYED')
app_outlier_col_2 <- c('CNT_CHILDREN', 'DAYS_BIRTH')
# Crear boxplots para app_outlier_col_1
plots1 <- lapply(app_outlier_col_1, function(var) {
ggplot(datos, aes(y = .data[[var]])) +
geom_boxplot(fill = "lightblue", color = "black") +
labs(title = var, y = "") +
theme_minimal()
})
# Crear boxplots para app_outlier_col_2
plots2 <- lapply(app_outlier_col_2, function(var) {
ggplot(datos, aes(y = .data[[var]])) +
geom_boxplot(fill = "lightblue", color = "black") +
labs(title = var, y = "") +
theme_minimal()
})
# Mostrar todos los gráficos en una sola figura
grid.arrange(grobs = c(plots1, plots2), ncol = 4)
# eliminamos la categoria unknown de NAME_FAMILY_STATUS al no tener ninguna observacion
datos <- datos |> filter(NAME_FAMILY_STATUS != "Unknown")
datos$NAME_FAMILY_STATUS <- droplevels(datos$NAME_FAMILY_STATUS)
#eliminamos la categoria de "60 above" y "50-60" para YEARS_EMPLOYED
datos <- datos[!datos$EMPLOYMENT_YEAR %in% c("50-60", "60 above"), ]
# eliminamos la categoria XNA que tiene 0 observaciones
datos <- datos[datos$CODE_GENDER != "XNA", ]
datos$CODE_GENDER <- droplevels(datos$CODE_GENDER)
# hemos tenido problemas con las personas que estan desempleadas, hay que asignarlas un valor, por tanto las asignamos al valor "0-5"
datos$EMPLOYMENT_YEAR <- ifelse(
datos$NAME_INCOME_TYPE == "Unemployed", "0", as.character(datos$EMPLOYMENT_YEAR))
datos$EMPLOYMENT_YEAR <- as.factor(datos$EMPLOYMENT_YEAR)
# aquellas observaciones que ya no se han podido sustituir ya sea por valores atipicos o causen problemas se eliminan
datos <- na.omit(datos)Tablas de contingencia
tb_conting <- function(df, x, vec){
for(i in seq_along(vec)){
cat("\nTabla de Contingencia para:", vec[i], "\n")
# Crear tabla de contingencia con nombres de filas y columnas
tab <- table(df[[x]], df[[vec[i]]])
dimnames(tab) <- list(TARGET = levels(factor(df[[x]])), Variable = levels(factor(df[[vec[i]]])))
print(tab)
cat("\nTest de Chi-Cuadrado:\n")
chi_test <- chisq.test(tab)
print(chi_test)
cat("\n--------------------------\n")
}
}
# Llamada a la función, suponiendo que df es tu base de datos
tb_conting(datos, "TARGET", contact_col) # Puedes probar con col_Doc o ext también
Tabla de Contingencia para: FLAG_MOBIL
Variable
TARGET 0 1
0 1 230098
1 0 21832
Test de Chi-Cuadrado:Warning in chisq.test(tab): Chi-squared approximation may be incorrect
Pearson's Chi-squared test with Yates' continuity correction
data: tab
X-squared = 2.7239e-22, df = 1, p-value = 1
--------------------------
Tabla de Contingencia para: FLAG_EMP_PHONE
Variable
TARGET 0 1
0 25 230074
1 9 21823
Test de Chi-Cuadrado:Warning in chisq.test(tab): Chi-squared approximation may be incorrect
Pearson's Chi-squared test with Yates' continuity correction
data: tab
X-squared = 11.463, df = 1, p-value = 0.0007101
--------------------------
Tabla de Contingencia para: FLAG_WORK_PHONE
Variable
TARGET 0 1
0 174752 55347
1 15931 5901
Test de Chi-Cuadrado:
Pearson's Chi-squared test with Yates' continuity correction
data: tab
X-squared = 95.784, df = 1, p-value < 2.2e-16
--------------------------
Tabla de Contingencia para: FLAG_CONT_MOBILE
Variable
TARGET 0 1
0 490 229609
1 43 21789
Test de Chi-Cuadrado:
Pearson's Chi-squared test with Yates' continuity correction
data: tab
X-squared = 0.17177, df = 1, p-value = 0.6785
--------------------------
Tabla de Contingencia para: FLAG_PHONE
Variable
TARGET 0 1
0 165455 64644
1 16534 5298
Test de Chi-Cuadrado:
Pearson's Chi-squared test with Yates' continuity correction
data: tab
X-squared = 145.42, df = 1, p-value < 2.2e-16
--------------------------
Tabla de Contingencia para: FLAG_EMAIL
Variable
TARGET 0 1
0 215396 14703
1 20550 1282
Test de Chi-Cuadrado:
Pearson's Chi-squared test with Yates' continuity correction
data: tab
X-squared = 8.9079, df = 1, p-value = 0.002839
--------------------------tb_conting(datos, "TARGET", col_Doc) # Puedes probar con col_Doc o ext también
Tabla de Contingencia para: FLAG_DOCUMENT_2
Variable
TARGET 0 1
0 230090 9
1 21828 4
Test de Chi-Cuadrado:Warning in chisq.test(tab): Chi-squared approximation may be incorrect
Pearson's Chi-squared test with Yates' continuity correction
data: tab
X-squared = 5.4751, df = 1, p-value = 0.01929
--------------------------
Tabla de Contingencia para: FLAG_DOCUMENT_3
Variable
TARGET 0 1
0 55752 174347
1 3938 17894
Test de Chi-Cuadrado:
Pearson's Chi-squared test with Yates' continuity correction
data: tab
X-squared = 422.5, df = 1, p-value < 2.2e-16
--------------------------
Tabla de Contingencia para: FLAG_DOCUMENT_4
Variable
TARGET 0 1
0 230079 20
1 21832 0
Test de Chi-Cuadrado:Warning in chisq.test(tab): Chi-squared approximation may be incorrect
Pearson's Chi-squared test with Yates' continuity correction
data: tab
X-squared = 0.96074, df = 1, p-value = 0.327
--------------------------
Tabla de Contingencia para: FLAG_DOCUMENT_5
Variable
TARGET 0 1
0 226355 3744
1 21483 349
Test de Chi-Cuadrado:
Pearson's Chi-squared test with Yates' continuity correction
data: tab
X-squared = 0.084646, df = 1, p-value = 0.7711
--------------------------
Tabla de Contingencia para: FLAG_DOCUMENT_6
Variable
TARGET 0 1
0 228048 2051
1 21698 134
Test de Chi-Cuadrado:
Pearson's Chi-squared test with Yates' continuity correction
data: tab
X-squared = 17.548, df = 1, p-value = 2.802e-05
--------------------------
Tabla de Contingencia para: FLAG_DOCUMENT_7
Variable
TARGET 0 1
0 230052 47
1 21829 3
Test de Chi-Cuadrado:Warning in chisq.test(tab): Chi-squared approximation may be incorrect
Pearson's Chi-squared test with Yates' continuity correction
data: tab
X-squared = 0.17534, df = 1, p-value = 0.6754
--------------------------
Tabla de Contingencia para: FLAG_DOCUMENT_8
Variable
TARGET 0 1
0 207499 22600
1 20016 1816
Test de Chi-Cuadrado:
Pearson's Chi-squared test with Yates' continuity correction
data: tab
X-squared = 51.349, df = 1, p-value = 7.732e-13
--------------------------
Tabla de Contingencia para: FLAG_DOCUMENT_9
Variable
TARGET 0 1
0 229016 1083
1 21759 73
Test de Chi-Cuadrado:
Pearson's Chi-squared test with Yates' continuity correction
data: tab
X-squared = 7.8141, df = 1, p-value = 0.005184
--------------------------
Tabla de Contingencia para: FLAG_DOCUMENT_10
Variable
TARGET 0 1
0 230093 6
1 21832 0
Test de Chi-Cuadrado:Warning in chisq.test(tab): Chi-squared approximation may be incorrect
Pearson's Chi-squared test with Yates' continuity correction
data: tab
X-squared = 0.00083827, df = 1, p-value = 0.9769
--------------------------
Tabla de Contingencia para: FLAG_DOCUMENT_11
Variable
TARGET 0 1
0 228973 1126
1 21757 75
Test de Chi-Cuadrado:
Pearson's Chi-squared test with Yates' continuity correction
data: tab
X-squared = 8.6322, df = 1, p-value = 0.003303
--------------------------
Tabla de Contingencia para: FLAG_DOCUMENT_12
Variable
TARGET 0 1
0 230097 2
1 21832 0
Test de Chi-Cuadrado:Warning in chisq.test(tab): Chi-squared approximation may be incorrect
Pearson's Chi-squared test with Yates' continuity correction
data: tab
X-squared = 5.4479e-22, df = 1, p-value = 1
--------------------------
Tabla de Contingencia para: FLAG_DOCUMENT_13
Variable
TARGET 0 1
0 229063 1036
1 21803 29
Test de Chi-Cuadrado:
Pearson's Chi-squared test with Yates' continuity correction
data: tab
X-squared = 46.973, df = 1, p-value = 7.198e-12
--------------------------
Tabla de Contingencia para: FLAG_DOCUMENT_14
Variable
TARGET 0 1
0 229244 855
1 21802 30
Test de Chi-Cuadrado:
Pearson's Chi-squared test with Yates' continuity correction
data: tab
X-squared = 30.57, df = 1, p-value = 3.221e-08
--------------------------
Tabla de Contingencia para: FLAG_DOCUMENT_15
Variable
TARGET 0 1
0 229748 351
1 21821 11
Test de Chi-Cuadrado:
Pearson's Chi-squared test with Yates' continuity correction
data: tab
X-squared = 13.8, df = 1, p-value = 0.0002033
--------------------------
Tabla de Contingencia para: FLAG_DOCUMENT_16
Variable
TARGET 0 1
0 227248 2851
1 21682 150
Test de Chi-Cuadrado:
Pearson's Chi-squared test with Yates' continuity correction
data: tab
X-squared = 51.147, df = 1, p-value = 8.571e-13
--------------------------
Tabla de Contingencia para: FLAG_DOCUMENT_17
Variable
TARGET 0 1
0 230020 79
1 21830 2
Test de Chi-Cuadrado:
Pearson's Chi-squared test with Yates' continuity correction
data: tab
X-squared = 3.1869, df = 1, p-value = 0.07423
--------------------------
Tabla de Contingencia para: FLAG_DOCUMENT_18
Variable
TARGET 0 1
0 227768 2331
1 21690 142
Test de Chi-Cuadrado:
Pearson's Chi-squared test with Yates' continuity correction
data: tab
X-squared = 26.604, df = 1, p-value = 2.497e-07
--------------------------
Tabla de Contingencia para: FLAG_DOCUMENT_19
Variable
TARGET 0 1
0 229932 167
1 21820 12
Test de Chi-Cuadrado:
Pearson's Chi-squared test with Yates' continuity correction
data: tab
X-squared = 0.64075, df = 1, p-value = 0.4234
--------------------------
Tabla de Contingencia para: FLAG_DOCUMENT_20
Variable
TARGET 0 1
0 229957 142
1 21819 13
Test de Chi-Cuadrado:
Pearson's Chi-squared test with Yates' continuity correction
data: tab
X-squared = 2.9034e-28, df = 1, p-value = 1
--------------------------
Tabla de Contingencia para: FLAG_DOCUMENT_21
Variable
TARGET 0 1
0 230010 89
1 21818 14
Test de Chi-Cuadrado:
Pearson's Chi-squared test with Yates' continuity correction
data: tab
X-squared = 2.5675, df = 1, p-value = 0.1091
--------------------------Analisis de Datos
En un principio me interesa saber cuales son las variables mas importantes a la hora de predecir si alguien va a devovler el pago o no, por tanto realizamos un modelo con todas las variables y hacemos el ANOVA para ver cuales son las mas significativas
#anova(lm(TARGET~.,data=datos))
anova_results <- anova(lm(TARGET ~ ., data = datos))
# Ordenar por la suma de cuadrados (Sum Sq) en orden descendente
(anova_sorted <- anova_results[order(-anova_results$`Sum Sq`), ])Analysis of Variance Table
Response: TARGET
Df Sum Sq Mean Sq F value Pr(>F)
Residuals 251666 18566.3 0.07
EXT_SOURCE_3 1 324.4 324.44 4397.8180 < 2.2e-16 ***
EXT_SOURCE_2 1 320.4 320.40 4343.0108 < 2.2e-16 ***
DAYS_BIRTH 1 61.4 61.44 832.7706 < 2.2e-16 ***
AMT_GOODS_PRICE 1 57.1 57.05 773.3386 < 2.2e-16 ***
FLAG_OWN_CAR 1 51.8 51.78 701.8813 < 2.2e-16 ***
EXT_SOURCE_1 1 49.1 49.13 665.9867 < 2.2e-16 ***
CODE_GENDER 1 47.0 47.01 637.2217 < 2.2e-16 ***
DAYS_EMPLOYED 1 42.2 42.17 571.6829 < 2.2e-16 ***
REGION_RATING_CLIENT 2 41.8 20.90 283.3041 < 2.2e-16 ***
NAME_EDUCATION_TYPE 4 39.6 9.89 134.0456 < 2.2e-16 ***
AMT_INCOME_TOTAL 1 29.3 29.31 397.2466 < 2.2e-16 ***
NAME_INCOME_TYPE 7 28.2 4.03 54.6094 < 2.2e-16 ***
AMT_CREDIT_RANGE 10 26.1 2.61 35.3973 < 2.2e-16 ***
NAME_CONTRACT_TYPE 1 26.0 25.97 352.0502 < 2.2e-16 ***
NAME_FAMILY_STATUS 4 23.3 5.81 78.8051 < 2.2e-16 ***
AMT_CREDIT 1 21.6 21.62 293.1172 < 2.2e-16 ***
ORGANIZATION_TYPE 56 20.9 0.37 5.0688 < 2.2e-16 ***
DAYS_ID_PUBLISH 1 18.4 18.44 250.0106 < 2.2e-16 ***
OCCUPATION_TYPE 18 17.3 0.96 12.9905 < 2.2e-16 ***
REGION_POPULATION_RELATIVE 1 14.8 14.80 200.6091 < 2.2e-16 ***
NAME_HOUSING_TYPE 5 11.8 2.36 32.0557 < 2.2e-16 ***
FLAG_WORK_PHONE 1 10.0 9.99 135.4386 < 2.2e-16 ***
DEF_30_CNT_SOCIAL_CIRCLE 1 9.9 9.88 133.9102 < 2.2e-16 ***
REG_CITY_NOT_LIVE_CITY 1 8.0 8.05 109.0890 < 2.2e-16 ***
DAYS_REGISTRATION 1 6.9 6.93 93.8974 < 2.2e-16 ***
REGION_RATING_CLIENT_W_CITY 2 6.7 3.36 45.5136 < 2.2e-16 ***
FLAG_DOCUMENT_3 1 5.3 5.32 72.1344 < 2.2e-16 ***
AGE_GROUP 4 4.8 1.20 16.3134 2.285e-13 ***
AMT_ANNUITY 1 4.7 4.71 63.8411 1.354e-15 ***
EMPLOYMENT_YEAR 5 4.2 0.85 11.4648 4.349e-11 ***
FLAG_PHONE 1 3.6 3.58 48.5626 3.207e-12 ***
OWN_CAR_AGE 1 2.9 2.91 39.3902 3.476e-10 ***
CNT_CHILDREN 1 2.7 2.70 36.5430 1.495e-09 ***
DAYS_LAST_PHONE_CHANGE 1 2.5 2.55 34.5516 4.156e-09 ***
NAME_TYPE_SUITE 7 2.5 0.35 4.7570 2.331e-05 ***
FLAG_DOCUMENT_18 1 2.2 2.19 29.7456 4.931e-08 ***
FLAG_DOCUMENT_16 1 2.0 2.03 27.5072 1.566e-07 ***
WEEKDAY_APPR_PROCESS_START 6 1.7 0.28 3.7868 0.0008958 ***
REG_CITY_NOT_WORK_CITY 1 1.6 1.59 21.5392 3.468e-06 ***
WALLSMATERIAL_MODE 7 1.5 0.22 2.9613 0.0041933 **
HOUR_APPR_PROCESS_START 1 1.2 1.21 16.4369 5.031e-05 ***
AMT_REQ_CREDIT_BUREAU_QRT 1 1.1 1.11 15.0008 0.0001075 ***
APARTMENTS_AVG 1 1.0 1.04 14.0331 0.0001797 ***
FLOORSMAX_AVG 1 1.0 0.97 13.1753 0.0002837 ***
FLAG_DOCUMENT_5 1 0.9 0.93 12.6546 0.0003747 ***
FLAG_DOCUMENT_2 1 0.9 0.92 12.5059 0.0004058 ***
FONDKAPREMONT_MODE 4 0.9 0.23 3.0499 0.0159292 *
AMT_INCOME_RANGE 10 0.9 0.09 1.1889 0.2925494
OBS_30_CNT_SOCIAL_CIRCLE 1 0.8 0.80 10.8412 0.0009928 ***
YEARS_EMPLOYED 1 0.6 0.57 7.7489 0.0053749 **
AMT_REQ_CREDIT_BUREAU_WEEK 1 0.5 0.52 6.9830 0.0082291 **
YEARS_BUILD_AVG 1 0.5 0.48 6.4516 0.0110859 *
FLAG_DOCUMENT_14 1 0.5 0.47 6.4222 0.0112710 *
FLAG_EMAIL 1 0.5 0.45 6.1266 0.0133167 *
EMERGENCYSTATE_MODE 2 0.4 0.22 3.0362 0.0480175 *
FLAG_DOCUMENT_13 1 0.4 0.43 5.8133 0.0159061 *
FLAG_DOCUMENT_8 1 0.4 0.43 5.7729 0.0162760 *
FLAG_CONT_MOBILE 1 0.4 0.42 5.6940 0.0170236 *
YEARS_BEGINEXPLUATATION_AVG 1 0.4 0.36 4.8943 0.0269458 *
NONLIVINGAREA_MODE 1 0.3 0.26 3.5766 0.0585992 .
FLAG_DOCUMENT_15 1 0.2 0.23 3.1349 0.0766321 .
AMT_REQ_CREDIT_BUREAU_MON 1 0.2 0.23 3.1288 0.0769197 .
HOUSETYPE_MODE 3 0.2 0.07 0.9618 0.4096356
COMMONAREA_AVG 1 0.2 0.19 2.5410 0.1109255
FLAG_DOCUMENT_6 1 0.2 0.18 2.4017 0.1212067
FLAG_OWN_REALTY 1 0.2 0.16 2.2253 0.1357670
FLAG_DOCUMENT_9 1 0.2 0.16 2.1759 0.1401869
AGE 1 0.1 0.13 1.8259 0.1766090
ELEVATORS_AVG 1 0.1 0.13 1.8056 0.1790337
DEF_60_CNT_SOCIAL_CIRCLE 1 0.1 0.13 1.7604 0.1845784
FLAG_DOCUMENT_17 1 0.1 0.13 1.7157 0.1902462
BASEMENTAREA_AVG 1 0.1 0.12 1.6208 0.2029832
LIVINGAPARTMENTS_MODE 1 0.1 0.11 1.4872 0.2226486
LIVE_REGION_NOT_WORK_REGION 1 0.1 0.10 1.4037 0.2361015
NONLIVINGAPARTMENTS_MODE 1 0.1 0.10 1.3907 0.2382909
COMMONAREA_MEDI 1 0.1 0.10 1.3692 0.2419439
ENTRANCES_AVG 1 0.1 0.10 1.3629 0.2430302
LIVINGAPARTMENTS_MEDI 1 0.1 0.10 1.3367 0.2476193
LIVE_CITY_NOT_WORK_CITY 1 0.1 0.09 1.2457 0.2643816
LANDAREA_MODE 1 0.1 0.09 1.2198 0.2694062
LANDAREA_MEDI 1 0.1 0.08 1.0380 0.3082776
YEARS_BEGINEXPLUATATION_MEDI 1 0.1 0.08 1.0232 0.3117562
LANDAREA_AVG 1 0.1 0.07 0.9787 0.3225119
OBS_60_CNT_SOCIAL_CIRCLE 1 0.1 0.07 0.9415 0.3318868
FLAG_DOCUMENT_11 1 0.1 0.06 0.8306 0.3620880
ENTRANCES_MODE 1 0.1 0.06 0.8116 0.3676632
BASEMENTAREA_MEDI 1 0.1 0.06 0.7902 0.3740473
FLAG_DOCUMENT_19 1 0.1 0.05 0.7154 0.3976610
FLAG_DOCUMENT_10 1 0.0 0.04 0.5863 0.4438568
LIVINGAREA_MEDI 1 0.0 0.04 0.5707 0.4499968
ELEVATORS_MODE 1 0.0 0.04 0.5581 0.4550200
SK_ID_CURR 1 0.0 0.04 0.5548 0.4563665
YEARS_BUILD_MEDI 1 0.0 0.03 0.4680 0.4939106
FLAG_DOCUMENT_4 1 0.0 0.03 0.4223 0.5158133
NONLIVINGAREA_AVG 1 0.0 0.02 0.3273 0.5672391
FLAG_DOCUMENT_20 1 0.0 0.02 0.3222 0.5702855
LIVINGAREA_AVG 1 0.0 0.02 0.3196 0.5718629
NONLIVINGAPARTMENTS_MEDI 1 0.0 0.02 0.2963 0.5861934
APARTMENTS_MODE 1 0.0 0.02 0.2934 0.5880228
FLOORSMAX_MODE 1 0.0 0.02 0.2931 0.5882637
FLAG_MOBIL 1 0.0 0.02 0.2601 0.6100336
ENTRANCES_MEDI 1 0.0 0.01 0.2030 0.6522739
FLOORSMAX_MEDI 1 0.0 0.01 0.2024 0.6527951
FLAG_DOCUMENT_7 1 0.0 0.01 0.1791 0.6721436
YEARS_BUILD_MODE 1 0.0 0.01 0.1750 0.6757463
AMT_REQ_CREDIT_BUREAU_YEAR 1 0.0 0.01 0.1715 0.6787674
FLAG_DOCUMENT_21 1 0.0 0.01 0.1643 0.6851913
FLOORSMIN_AVG 1 0.0 0.01 0.1481 0.7003682
LIVINGAREA_MODE 1 0.0 0.01 0.1242 0.7245029
TOTALAREA_MODE 1 0.0 0.01 0.0983 0.7538283
FLAG_DOCUMENT_12 1 0.0 0.01 0.0856 0.7698666
FLAG_EMP_PHONE 1 0.0 0.01 0.0801 0.7770995
YEARS_BEGINEXPLUATATION_MODE 1 0.0 0.01 0.0787 0.7790962
ELEVATORS_MEDI 1 0.0 0.00 0.0570 0.8112549
NONLIVINGAREA_MEDI 1 0.0 0.00 0.0412 0.8391866
FLOORSMIN_MODE 1 0.0 0.00 0.0403 0.8408954
APARTMENTS_MEDI 1 0.0 0.00 0.0268 0.8698642
REG_REGION_NOT_LIVE_REGION 1 0.0 0.00 0.0207 0.8857061
LIVINGAPARTMENTS_AVG 1 0.0 0.00 0.0157 0.9003191
AMT_REQ_CREDIT_BUREAU_HOUR 1 0.0 0.00 0.0137 0.9066605
FLOORSMIN_MEDI 1 0.0 0.00 0.0099 0.9207980
COMMONAREA_MODE 1 0.0 0.00 0.0069 0.9340282
BASEMENTAREA_MODE 1 0.0 0.00 0.0039 0.9504972
AMT_REQ_CREDIT_BUREAU_DAY 1 0.0 0.00 0.0004 0.9844991
REG_REGION_NOT_WORK_REGION 1 0.0 0.00 0.0001 0.9924951
NONLIVINGAPARTMENTS_AVG 1 0.0 0.00 0.0001 0.9926282
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1EXT_SOURCE_3 AMT_GOODS_PRICE FLAG_OWN_CAR EXT_SOURCE_1 CODE_GENDER DAYS_BIRTH NAME_EDUCATION_TYPE DAYS_EMPLOYED AMT_CREDIT NAME_INCOME_TYPE EXT_SOURCE_2 NAME_CONTRACT_TYPE OCCUPATION_TYPE NAME_FAMILY_STATUS AMT_CREDIT_RANGE
# Contar la frecuencia de cada categoría en la variable TARGET
Imbalance <- as.data.frame(table(datos$TARGET))
colnames(Imbalance) <- c("Loan_Repayment_Status", "Count")
# Reemplazar valores 0 y 1 con etiquetas significativas
Imbalance$Loan_Repayment_Status <- factor(Imbalance$Loan_Repayment_Status,
levels = c(0,1),
labels = c("Repayer", "Defaulter"))
# Calcular el porcentaje y crear la etiqueta
Imbalance$Percent <- Imbalance$Count / sum(Imbalance$Count) * 100
Imbalance$Label <- paste0(round(Imbalance$Percent, 1), "%")
# Crear el gráfico de barras con etiquetas de porcentaje
ggplot(Imbalance, aes(x = Loan_Repayment_Status, y = Count, fill = Loan_Repayment_Status)) +
geom_bar(stat = "identity") +
geom_text(aes(label = Label), vjust = -0.5) + # Añadir etiquetas encima de las barras
scale_fill_manual(values = c("green", "red")) +
labs(title = "Imbalance Plotting",
x = "Loan Repayment Status",
y = "Count of Repayers & Defaulters") +
theme_minimal()
definimos una funcion que dado una variable nos de un histograma con los pagos devueltos y no devueltos segun la variable
# Definir la función
plot_loan_repayment <- function(df, variable) {
# Verificar que la variable existe
if (!(variable %in% colnames(df))) {
stop("La variable especificada no existe en el dataframe.")
}
# Crear dataframe de trabajo
df_plot <- df[, c(variable, "TARGET")]
# Convertir TARGET a factor con etiquetas
df_plot$TARGET <- factor(df_plot$TARGET, levels = c(0, 1), labels = c("Repayer", "Defaulter"))
# Calcular proporciones por categoría
df_prop <- df_plot %>%
group_by(.data[[variable]], TARGET) %>%
summarise(n = n(), .groups = "drop") %>%
group_by(.data[[variable]]) %>%
mutate(pct = n / sum(n) * 100)
# Graficar con porcentajes
ggplot(df_prop, aes_string(x = variable, y = "pct", fill = "TARGET")) +
geom_bar(stat = "identity", position = "dodge") +
labs(
title = paste("Distribución porcentual de", variable, "según estado de pago"),
x = variable, y = "Porcentaje (%)"
) +
scale_fill_manual(values = c("green", "red")) +
scale_x_discrete(guide = guide_axis(angle = 45)) +
theme_minimal()
}Graficar variables categoricas
# Ejemplo de uso con la variable FLAG_OWN_CAR
plot_loan_repayment(datos, "FLAG_OWN_CAR")Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
ℹ Please use tidy evaluation idioms with `aes()`.
ℹ See also `vignette("ggplot2-in-packages")` for more information.
plot_loan_repayment(datos, "CODE_GENDER")
plot_loan_repayment(datos, "NAME_CONTRACT_TYPE")
plot_loan_repayment(datos, "NAME_EDUCATION_TYPE")
plot_loan_repayment(datos, "NAME_INCOME_TYPE")
plot_loan_repayment(datos, "AMT_CREDIT_RANGE")
plot_loan_repayment(datos, "NAME_FAMILY_STATUS")
plot_loan_repayment(datos, "ORGANIZATION_TYPE")
plot_loan_repayment(datos, "OCCUPATION_TYPE")
plot_loan_repayment(datos, "NAME_HOUSING_TYPE")
plot_loan_repayment(datos, "EMPLOYMENT_YEAR")
plot_loan_repayment(datos, "FLAG_DOCUMENT_3")
plot_loan_repayment(datos, "NAME_TYPE_SUITE")
Graficar variables continuas
graficar_variable <- function(data, variable) {
# Calcular los porcentajes por clase
porcentajes <- data %>%
group_by(TARGET) %>%
summarise(n = n()) %>%
mutate(porc = paste0(round(100 * n / sum(n), 1), "%"))
# Crear etiquetas personalizadas
levels_target <- sort(unique(data$TARGET))
etiquetas <- paste0(
ifelse(levels_target == 0, "Repayers", "Defaulters"),
" (", porcentajes$porc, ")"
)
# Graficar con los porcentajes en la leyenda
ggplot(data, aes(x = .data[[variable]], color = as.factor(TARGET))) +
geom_density(size = 1) +
labs(x = variable, y = "Densidad", title = paste("Distribución de", variable, "según TARGET")) +
scale_color_manual(
values = c("blue", "red"),
labels = etiquetas,
name = "TARGET"
) +
theme_minimal()
}# Ejemplo de uso con la variable "AMT_CREDIT"
graficar_variable(datos, "AMT_CREDIT")Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
# Ejemplo de uso con la variable "AMT_CREDIT"
graficar_variable(datos, "DAYS_BIRTH")
graficar_variable(datos, "AMT_GOODS_PRICE")
graficar_variable(datos, "DAYS_EMPLOYED")
graficar_variable(datos, "DAYS_LAST_PHONE_CHANGE")
graficar_variable(datos, "AMT_INCOME_TOTAL")
graficar_variable(datos, "AGE")
Guardar base de datos depurada para modelos
primero eliminamos las variables menos significativas, y nos quedamos con las mas significativas
variables_significativas <- c("EXT_SOURCE_3", "EXT_SOURCE_2", "DAYS_BIRTH", "AMT_GOODS_PRICE","FLAG_OWN_CAR", "EXT_SOURCE_1", "CODE_GENDER", "NAME_EDUCATION_TYPE", "DAYS_EMPLOYED", "REGION_RATING_CLIENT", "AMT_CREDIT", "NAME_INCOME_TYPE", "NAME_CONTRACT_TYPE", "AMT_CREDIT_RANGE","REGION_POPULATION_RELATIVE", "NAME_HOUSING_TYPE", "FLAG_WORK_PHONE","DEF_30_CNT_SOCIAL_CIRCLE", "REG_CITY_NOT_LIVE_CITY", "DAYS_REGISTRATION", "REGION_RATING_CLIENT_W_CITY", "FLAG_DOCUMENT_3", "AGE_GROUP", "EMPLOYMENT_YEAR", "FLAG_PHONE", "OWN_CAR_AGE", "CNT_CHILDREN", "DAYS_LAST_PHONE_CHANGE", "FLAG_DOCUMENT_18", "NAME_TYPE_SUITE", "FLAG_DOCUMENT_16", "WEEKDAY_APPR_PROCESS_START", "REG_CITY_NOT_WORK_CITY", "AMT_ANNUITY", "WALLSMATERIAL_MODE", "AMT_INCOME_TOTAL", "HOUR_APPR_PROCESS_START", "AMT_REQ_CREDIT_BUREAU_QRT", "APARTMENTS_AVG", "FLOORSMAX_AVG", "FLAG_DOCUMENT_5", "FLAG_DOCUMENT_2", "FONDKAPREMONT_MODE", "OBS_30_CNT_SOCIAL_CIRCLE", "YEARS_EMPLOYED","TARGET")
datos<- datos[,variables_significativas]
# eliminamos los NA faltantes, estos se deben a valores atipicos que dan problemas
#guardamos en una base de datos los datos, asi podemos seguir con el TFG sin saturar el PC
save(datos,file="DatosDepurados.RDa")